{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 0,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"jumbotron\">\n",
    "    <div class=\"row\">\n",
    "        <div class=\"col-md-12\">\n",
    "            <p class=\"display-1 h1\">Transformer</p>\n",
    "            <hr class=\"my-4\">\n",
    "            <p>主讲：李岩</p>\n",
    "            <p>管理学院</p>\n",
    "            <p>liyan@cumtb.edu.cn</p>\n",
    "        </div>\n",
    "    </div>\n",
    "    <div class=\"row\">\n",
    "        <div class=\"col-md-6\">\n",
    "            <img src=\"../img/9_attention_mechanisms/q-transformer.jpg\" width=75%>\n",
    "        </div>\n",
    "        <div class=\"col-md-6\">\n",
    "            <img src=\"../img/9_attention_mechanisms/RealTransformer.jpg\">\n",
    "        </div>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 为什么需要Transformer？\n",
    "\n",
    "**之前的模型**：\n",
    "- 仍然依赖**循环神经网络**实现输入表示\n",
    "- 自注意力只是作为辅助机制\n",
    "\n",
    "**Transformer的突破**：\n",
    "- **完全基于注意力机制**\n",
    "- **没有任何卷积层或循环神经网络层**\n",
    "- 这是第一个完全基于注意力的架构\n",
    "\n",
    "**影响**：\n",
    "- 最初应用于文本数据的序列到序列学习\n",
    "- 现在已推广到各种领域：\n",
    "  - 语言（BERT、GPT）\n",
    "  - 视觉（Vision Transformer）\n",
    "  - 语音（Whisper）\n",
    "  - 强化学习\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Transformer架构概览\n",
    "\n",
    "### 整体结构\n",
    "\n",
    "**Transformer的组成**：\n",
    "- **编码器（Encoder）**：处理输入序列\n",
    "- **解码器（Decoder）**：生成输出序列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"../img/9_attention_mechanisms/transformer.svg\" width=50%></center>\n",
    "\n",
    "**图1：Transformer架构**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 输入处理流程\n",
    "\n",
    "**编码器输入**：\n",
    "1. **词嵌入**：将源序列的词元转换为向量\n",
    "2. **位置编码**：添加位置信息\n",
    "3. **缩放**：嵌入值乘以$\\sqrt{d}$（平衡量级）\n",
    "\n",
    "**解码器输入**：\n",
    "1. **词嵌入**：将目标序列的词元转换为向量\n",
    "2. **位置编码**：添加位置信息\n",
    "3. **缩放**：嵌入值乘以$\\sqrt{d}$\n",
    "\n",
    "**关键点**：\n",
    "- 编码器和解码器使用**相同的位置编码函数**\n",
    "- 但应用于不同的序列（源序列和目标序列）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 5,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### 基于位置的前馈网络\n",
    "\n",
    "**核心思想**：\n",
    "- 对序列中**所有位置**的表示进行变换\n",
    "- 使用**同一个多层感知机（MLP）**\n",
    "- 这就是\"基于位置\"（positionwise）的含义\n",
    "\n",
    "**关键特点**：\n",
    "- 每个位置**独立**应用相同的MLP\n",
    "- 不涉及位置之间的交互\n",
    "- 类似于对每个位置做相同的特征变换\n",
    "\n",
    "**输入输出形状**：\n",
    "- 输入：`(batch_size, num_steps, num_hiddens)` - 批量大小，时间步数或序列长度，隐单元数或特征维度\n",
    "- 输出：`(batch_size, num_steps, ffn_num_outputs)` - 只改变**特征维度**，不改变序列长度\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 为什么需要基于位置的前馈网络？\n",
    "\n",
    "1. 注意力机制的局限\n",
    "    - 注意力层只执行**线性变换**（加权求和）\n",
    "    - 缺少**非线性建模**能力\n",
    "    - 无法学习复杂的特征交互\n",
    "2. 引入非线性\n",
    "    - 前馈网络提供**逐位置的非线性变换**\n",
    "    - 增强模型的表达能力\n",
    "    - 类似于卷积网络中的**1×1卷积**\n",
    "3. 保持并行性\n",
    "    - 每个位置**独立处理**，不依赖其他位置\n",
    "    - 可以**并行计算**，保持Transformer的高效性\n",
    "    - 不改变序列长度，只改变特征维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### PositionWiseFFN类实现\n",
    "\n",
    "**类的组件**：\n",
    "1. `self.dense1`：第一层全连接（扩展到更大的维度）\n",
    "2. `self.relu`：ReLU激活函数\n",
    "3. `self.dense2`：第二层全连接（压缩回原始维度）\n",
    "\n",
    "**设计模式**：\n",
    "- **扩展-压缩**：先扩展到更大维度，再压缩回来\n",
    "- 类似\"瓶颈\"结构\n",
    "- 增加模型的表达能力\n",
    "\n",
    "**注意**：\n",
    "- 所有位置使用**相同的权重**\n",
    "- 这是\"基于位置\"的关键\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 为什么“先升维再降维”能大幅提升表达力？\n",
    "\n",
    "- **几何视角**：把原始向量先映射到高维空间（如 512→2048），ReLU 在更高维度里可以“折叠”空间，产生大量分段线性区域；再投影回原维度，即可得到高度非线性的决策边界。\n",
    "- **参数视角**：两层全连接 512→2048→512 的参数量是 512×2048×2 ≈ 2 M，而同样维度的单层 512→512 仅 0.26 M；更多参数带来更丰富的函数族。\n",
    "- **Transformer 唯一非线性来源**：注意力只是线性加权，FFN 是子层里唯一的非线性升级；把中间层做“胖”可以确保整个块具备通用近似能力。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:21:05.487583Z",
     "iopub.status.busy": "2023-08-18T07:21:05.486906Z",
     "iopub.status.idle": "2023-08-18T07:21:05.492826Z",
     "shell.execute_reply": "2023-08-18T07:21:05.492063Z"
    },
    "origin_pos": 7,
    "slideshow": {
     "slide_type": "slide"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class PositionWiseFFN(nn.Module):\n",
    "    \"\"\"基于位置的前馈网络\"\"\"\n",
    "    def __init__(self, ffn_num_input, ffn_num_hiddens, ffn_num_outputs,\n",
    "                 **kwargs):\n",
    "        super(PositionWiseFFN, self).__init__(**kwargs)\n",
    "        self.dense1 = nn.Linear(ffn_num_input, ffn_num_hiddens)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dense2 = nn.Linear(ffn_num_hiddens, ffn_num_outputs)\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.dense2(self.relu(self.dense1(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 残差连接和层规范化\n",
    "\n",
    "### 加法和规范化组件\n",
    "\n",
    "**组件位置**：\n",
    "- 在Transformer架构中的\"add&norm\"组件\n",
    "- 由**残差连接**和**层规范化**组成\n",
    "\n",
    "**重要性**：\n",
    "- 两者都是构建**有效深度架构**的关键\n",
    "- 使训练非常深的网络成为可能\n",
    "- Transformer可以堆叠6层、12层甚至更多层\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 为什么需要残差连接？\n",
    "\n",
    "**问题**：深度网络中的梯度消失\n",
    "- 随着网络加深，梯度会逐渐变小\n",
    "- 导致底层参数难以更新\n",
    "- 训练困难，性能下降\n",
    "\n",
    "**解决方案**：残差连接\n",
    "- 提供\"直通路径\"：$\\mathbf{x} + \\mathrm{sublayer}(\\mathbf{x})$\n",
    "- 梯度可以直接流回输入\n",
    "- 缓解梯度消失问题\n",
    "\n",
    "**数学形式**：\n",
    "$$\\mathbf{y} = \\mathrm{LayerNorm}(\\mathbf{x} + \\mathrm{Dropout}(\\mathrm{Sublayer}(\\mathbf{x})))$$\n",
    "\n",
    "**关键点**：\n",
    "- 即使$\\mathrm{Sublayer}(\\mathbf{x})$很小，$\\mathbf{x}$仍能传递信息\n",
    "- 允许网络学习恒等映射（如果最优解接近输入）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 为什么需要层规范化？\n",
    "\n",
    "**问题**：训练不稳定\n",
    "- 深层网络中激活值的分布会变化\n",
    "- 导致训练过程不稳定\n",
    "- 需要较小的学习率\n",
    "\n",
    "**解决方案**：层规范化\n",
    "- 对每个样本的特征维度进行规范化\n",
    "- 稳定激活值的分布\n",
    "- 允许使用更大的学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 层规范化完整计算图（逐 token）\n",
    "\n",
    "```mermaid\n",
    "graph TD\n",
    "    A[Input: x (vector，单一token)];\n",
    "    A --> B[μ = mean(x)];\n",
    "    B --> C[σ = sqrt(mean((x-μ)²) + ε)];\n",
    "    C --> D[x̂ = (x - μ) / σ];\n",
    "    D --> E[y = γ * x̂ + β];\n",
    "    E --> F[Output: y];\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 为什么不用Batch Normalization？\n",
    "\n",
    "- 序列长度可变：BN的均值方差维度随位置变化，推理时滑动均值失效。\n",
    "- batch太小：推理阶段往往batch=1，BN的统计量噪声极大。\n",
    "- token间无卷积通道概念：BN的“同一通道“假设不成立。\n",
    "- 残差分支需要稳定梯度：LN对 单个token 归一，与前后位置无关，更稳定。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"../img/9_attention_mechanisms/layerNormBatchNorm.jpg\" width=100%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "| 特征 (Feature) | CNN (BatchNorm) | Transformer (假设用 BatchNorm) |\n",
    "| :--- | :--- | :--- |\n",
    "| **归一化单元** | 按通道 (Per Channel) | 按特征维度 (Per Feature/Dimension) |\n",
    "| **统计量来源** | `N * H * W` 个值 (量大稳定) | `N` 个值 (量小且噪声大) |\n",
    "| **推理 (N=1)** | 使用训练好的稳定 `running_stats` | 失效 (方差为0或新位置无统计量) |\n",
    "| **关键假设** | 空间位置可交换 | 序列位置**不可**交换 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### AddNorm类实现\n",
    "\n",
    "**类的组件**：\n",
    "1. `self.dropout`：Dropout层（正则化）\n",
    "2. `self.ln`：层规范化层\n",
    "\n",
    "**forward方法**：\n",
    "- 输入：`X`（原始输入）和`Y`（子层输出）\n",
    "- 处理：`Dropout(Y) + X`（残差连接）\n",
    "- 输出：`LayerNorm(...)`（层规范化）\n",
    "\n",
    "**顺序**：\n",
    "1. 先应用Dropout到子层输出\n",
    "2. 然后与原始输入相加（残差连接）\n",
    "3. 最后应用层规范化\n",
    "\n",
    "**注意**：\n",
    "- 残差连接要求两个输入形状相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:21:05.547230Z",
     "iopub.status.busy": "2023-08-18T07:21:05.546508Z",
     "iopub.status.idle": "2023-08-18T07:21:05.553654Z",
     "shell.execute_reply": "2023-08-18T07:21:05.552629Z"
    },
    "origin_pos": 22,
    "slideshow": {
     "slide_type": "slide"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class AddNorm(nn.Module):\n",
    "    \"\"\"残差连接后进行层规范化\"\"\"\n",
    "    def __init__(self, normalized_shape, dropout, **kwargs):\n",
    "        super(AddNorm, self).__init__(**kwargs)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.ln = nn.LayerNorm(normalized_shape)\n",
    "\n",
    "    def forward(self, X, Y):\n",
    "        return self.ln(self.dropout(Y) + X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Transformer编码器\n",
    "\n",
    "### 编码器块（EncoderBlock）\n",
    "\n",
    "**编码器块的组成**：\n",
    "- 两个子层（sub-layer），每个子层都有残差连接和层规范化\n",
    "\n",
    "**子层1：多头自注意力**：\n",
    "- `self.attention`：多头自注意力模块\n",
    "- `self.addnorm1`：残差连接 + 层规范化\n",
    "- **作用**：让每个位置关注序列中的所有位置\n",
    "\n",
    "**子层2：基于位置的前馈网络**：\n",
    "- `self.ffn`：位置前馈网络\n",
    "- `self.addnorm2`：残差连接 + 层规范化\n",
    "- **作用**：增加非线性表达能力"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### EncoderBlock类实现\n",
    "\n",
    "**类的作用**：\n",
    "- 实现Transformer编码器中的一个层\n",
    "- 包含两个子层：多头自注意力和基于位置的前馈网络\n",
    "- 两个子层都使用了残差连接和紧随的层规范化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-08-18T07:21:05.580861Z"
    },
    "origin_pos": 31,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class EncoderBlock(nn.Module):\n",
    "    \"\"\"Transformer编码器块\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, use_bias=False, **kwargs):\n",
    "        super(EncoderBlock, self).__init__(**kwargs)\n",
    "        self.attention = d2l.MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout,\n",
    "            use_bias)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(\n",
    "            ffn_num_input, ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))\n",
    "        return self.addnorm2(Y, self.ffn(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2023-08-18T07:21:05.618289Z",
     "shell.execute_reply": "2023-08-18T07:21:05.616871Z"
    },
    "origin_pos": 40,
    "slideshow": {
     "slide_type": "slide"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerEncoder(d2l.Encoder):\n",
    "    \"\"\"Transformer编码器\"\"\"\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, use_bias=False, **kwargs):\n",
    "        super(TransformerEncoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\"block\"+str(i),\n",
    "                EncoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                             norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                             num_heads, dropout, use_bias))\n",
    "\n",
    "    def forward(self, X, valid_lens, *args):\n",
    "        # 因为位置编码值在-1和1之间，\n",
    "        # 因此嵌入值乘以嵌入维度的平方根进行缩放，\n",
    "        # 然后再与位置编码相加。\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self.attention_weights = [None] * len(self.blks)\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X = blk(X, valid_lens)\n",
    "            self.attention_weights[i] = blk.attention.attention.attention_weights\n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Transformer解码器\n",
    "\n",
    "### 解码器的结构\n",
    "\n",
    "**解码器的组成**：\n",
    "- 由**多个相同的层**组成（通常6层或更多）\n",
    "- 每个层包含**三个子层**（比编码器多一个）\n",
    "\n",
    "**三个子层**：\n",
    "1. **解码器自注意力**（掩蔽）\n",
    "   - 作用：理解已生成序列的内部关系\n",
    "   - 特点：掩蔽机制确保自回归属性\n",
    "\n",
    "2. **编码器-解码器注意力**\n",
    "   - 作用：关注输入序列的相关部分\n",
    "   - 特点：连接编码器和解码器\n",
    "\n",
    "3. **基于位置的前馈网络**\n",
    "   - 作用：增加非线性表达能力\n",
    "   - 特点：与编码器相同\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### 编码器 vs 解码器对比\n",
    "\n",
    "| 特性 | 编码器 | 解码器 |\n",
    "|------|--------|--------|\n",
    "| **子层数** | 2个 | 3个 |\n",
    "| **子层1** | 多头自注意力 | 掩蔽自注意力 |\n",
    "| **子层2** | 前馈网络 | 编码器-解码器注意力 |\n",
    "| **子层3** | 无 | 前馈网络 |\n",
    "| **输入** | 源序列 | 目标序列 |\n",
    "| **输出** | 编码表示 | 词汇表概率 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 为什么需要掩蔽？\n",
    "\n",
    "根本挑战：序列生成的因果性\n",
    "- 在序列生成中，模型在时间步$t$只能基于**过去和当前**的信息进行预测，不能访问**未来**信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### 正确的训练过程示例\n",
    "\n",
    "目标序列: \"Hello world!\"\n",
    "\n",
    "- 训练时的自回归分解：\n",
    "\n",
    "$$\n",
    "P(\\rm{Hello\\ world!}) = P(\\rm{H}) \\times P(\\rm{e}|\\rm{H}) \\times P(\\rm{l}|\\rm{He}) \\times ...\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- 逐步预测过程\n",
    "  \n",
    "输入: [SOS] → 预测: \"H\"\n",
    "\n",
    "输入: [SOS, \"H\"] → 预测: \"e\" \n",
    "\n",
    "输入: [SOS, \"H\", \"e\"] → 预测: \"l\"\n",
    "\n",
    "输入: [SOS, \"H\", \"e\", \"l\"] → 预测: \"l\"\n",
    "\n",
    "输入: [SOS, \"H\", \"e\", \"l\", \"l\"] → 预测: \"o\"\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**无掩蔽的问题：信息泄露**\n",
    "\n",
    "预测位置3 (\"l\") 时的注意力分布（错误情况）：\n",
    "\n",
    "位置1(\"H\"): ██ 20%\n",
    "\n",
    "位置2(\"e\"): ████ 30%  \n",
    "\n",
    "位置3(\"l\"): ████ 25%  ← 可以关注自己！\n",
    "\n",
    "位置4(\"l\"): ███ 15%   ← 错误地关注未来！\n",
    "\n",
    "位置5(\"o\"): ██ 10%    ← 错误地关注未来！\n",
    "\n",
    "问题：模型\"偷看\"了答案！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**有掩蔽的正确情况**\n",
    "\n",
    "因果掩码后的注意力分布：\n",
    "\n",
    "位置1(\"H\"): ██████ 60%\n",
    "\n",
    "位置2(\"e\"): ████ 40%  \n",
    "\n",
    "位置3(\"l\"):        0%  ← 不能关注自己和未来\n",
    "\n",
    "位置4(\"l\"):        0%  ← 被掩码屏蔽\n",
    "\n",
    "位置5(\"o\"):        0%  ← 被掩码屏蔽\n",
    "\n",
    "\n",
    "结果：只能基于已知信息进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 掩蔽的数学实现\n",
    "\n",
    "给定注意力权重矩阵 (5×5)，则掩蔽矩阵结构：\n",
    "\n",
    "[1, 0, 0, 0, 0]     位置1只能关注位置1\n",
    "\n",
    "[1, 1, 0, 0, 0]     位置2可关注位置1,2  \n",
    "\n",
    "[1, 1, 1, 0, 0]     位置3可关注位置1,2,3\n",
    "\n",
    "[1, 1, 1, 1, 0]     位置4可关注位置1,2,3,4\n",
    "\n",
    "[1, 1, 1, 1, 1]     位置5可关注位置1,2,3,4,5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**掩蔽矩阵 M**\n",
    "\n",
    "$$\n",
    "M[i][j] = \\begin{cases}\n",
    "0 & \\text{if } j \\leq i \\\\\n",
    "-\\infty & \\text{else}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "**掩蔽后的注意力计算**\n",
    "\n",
    "$$\n",
    "\\text{Attention}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T + M}{\\sqrt{d_k}}\\right)V\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 不使用掩蔽的严重后果\n",
    "\n",
    "1. 训练-推理不一致\n",
    "   - 训练时（无掩蔽）：模型可以访问未来信息\n",
    "   - 推理时（必须自回归）：模型只能基于已生成的序列\n",
    "   - 结果：性能显著下降！\n",
    "   \n",
    "2. 学习错误的依赖模式\n",
    "    - 错误学习：P(w | Hello world!) 依赖于看到完整序列\n",
    "    - 正确学习：P(w | Hello) 只能依赖已生成部分\n",
    "\n",
    "3. 梯度流动异常\n",
    "   - 无掩蔽会允许梯度从未来时间步反向传播，破坏因果结构。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 编码器-解码器注意力的作用\n",
    "\n",
    "**目的**：\n",
    "- 让解码器**关注编码器的输出**\n",
    "- 将输入序列的信息传递到输出序列\n",
    "\n",
    "**查询、键、值的来源**：\n",
    "- 查询：解码器自注意力的输出（解码器当前状态）\n",
    "- 键和值：编码器的输出（输入序列的表示）\n",
    "\n",
    "**类比**：\n",
    "- 就像翻译时，既要看已翻译的部分（自注意力），也要看原文（编码器-解码器注意力）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### DecoderBlock类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:21:05.648104Z",
     "iopub.status.busy": "2023-08-18T07:21:05.647725Z",
     "iopub.status.idle": "2023-08-18T07:21:05.662950Z",
     "shell.execute_reply": "2023-08-18T07:21:05.661933Z"
    },
    "origin_pos": 50,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class DecoderBlock(nn.Module):\n",
    "    \"\"\"解码器中第i个块\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, i, **kwargs):\n",
    "        super(DecoderBlock, self).__init__(**kwargs)\n",
    "        self.i = i\n",
    "        self.attention1 = MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.attention2 = MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens,\n",
    "                                   num_hiddens)\n",
    "        self.addnorm3 = AddNorm(norm_shape, dropout)\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        enc_outputs, enc_valid_lens = state[0], state[1]\n",
    "        # 训练阶段，输出序列的所有词元都在同一时间处理，\n",
    "        # 因此state[2][self.i]初始化为None。\n",
    "        # 预测阶段，输出序列是通过词元一个接着一个解码的，\n",
    "        # 因此state[2][self.i]包含着直到当前时间步第i个块解码的输出表示\n",
    "        if state[2][self.i] is None:\n",
    "            key_values = X\n",
    "        else:\n",
    "            key_values = torch.cat((state[2][self.i], X), axis=1)\n",
    "        state[2][self.i] = key_values\n",
    "        if self.training:\n",
    "            batch_size, num_steps, _ = X.shape\n",
    "            # dec_valid_lens的开头:(batch_size,num_steps),\n",
    "            # 其中每一行是[1,2,...,num_steps]\n",
    "            dec_valid_lens = torch.arange(\n",
    "                1, num_steps + 1, device=X.device).repeat(batch_size, 1)\n",
    "        else:\n",
    "            dec_valid_lens = None\n",
    "\n",
    "        # 自注意力\n",
    "        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)\n",
    "        Y = self.addnorm1(X, X2)\n",
    "        # 编码器－解码器注意力。\n",
    "        # enc_outputs的开头:(batch_size,num_steps,num_hiddens)\n",
    "        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)\n",
    "        Z = self.addnorm2(Y, Y2)\n",
    "        return self.addnorm3(Z, self.ffn(Z)), state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### DecoderBlock的forward方法详解\n",
    "\n",
    "**输入**：\n",
    "- `X`：当前解码器层的输入\n",
    "- `state`：解码器状态\n",
    "  - `state[0]`：编码器输出\n",
    "  - `state[1]`：编码器有效长度\n",
    "  - `state[2]`：各层的键值缓存\n",
    "\n",
    "**处理步骤**：\n",
    "\n",
    "1. **准备键值**：\n",
    "   - 训练时：使用当前输入`X`\n",
    "   - 预测时：拼接已生成的词元\n",
    "\n",
    "2. **解码器自注意力**：\n",
    "   - 查询：`X`\n",
    "   - 键和值：`key_values`（掩蔽）\n",
    "   - 输出：`X2`\n",
    "\n",
    "3. **编码器-解码器注意力**：\n",
    "   - 查询：自注意力的输出`Y`\n",
    "   - 键和值：编码器输出`enc_outputs`\n",
    "\n",
    "4. **前馈网络**：\n",
    "   - 对编码器-解码器注意力的输出应用MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### TransformerDecoder类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:21:05.691062Z",
     "iopub.status.busy": "2023-08-18T07:21:05.690657Z",
     "iopub.status.idle": "2023-08-18T07:21:05.706405Z",
     "shell.execute_reply": "2023-08-18T07:21:05.704976Z"
    },
    "origin_pos": 59,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerDecoder(d2l.AttentionDecoder):\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, **kwargs):\n",
    "        super(TransformerDecoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.num_layers = num_layers\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\"block\"+str(i),\n",
    "                DecoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                             norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                             num_heads, dropout, i))\n",
    "        self.dense = nn.Linear(num_hiddens, vocab_size)\n",
    "\n",
    "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
    "        return [enc_outputs, enc_valid_lens, [None] * self.num_layers]\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self._attention_weights = [[None] * len(self.blks) for _ in range (2)]\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X, state = blk(X, state)\n",
    "            # 解码器自注意力权重\n",
    "            self._attention_weights[0][\n",
    "                i] = blk.attention1.attention.attention_weights\n",
    "            # “编码器－解码器”自注意力权重\n",
    "            self._attention_weights[1][\n",
    "                i] = blk.attention2.attention.attention_weights\n",
    "        return self.dense(X), state\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 训练和预测阶段的区别\n",
    "\n",
    "**训练阶段**：\n",
    "- `state[2][self.i]`初始化为`None`\n",
    "- 所有词元在同一时间处理\n",
    "- `dec_valid_lens`：`[1, 2, ..., num_steps]`（掩蔽）\n",
    "\n",
    "**预测阶段**：\n",
    "- `state[2][self.i]`包含已生成的词元\n",
    "- 词元逐个生成\n",
    "- `dec_valid_lens = None`（由注意力模块处理掩蔽）\n",
    "\n",
    "**关键区别**：\n",
    "- 训练：并行处理，使用掩蔽\n",
    "- 预测：自回归生成，逐步构建\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 训练Transformer模型\n",
    "\n",
    "### 模型配置\n",
    "\n",
    "**架构设置**：\n",
    "- 编码器和解码器都是**2层**\n",
    "- 都使用**4头注意力**\n",
    "\n",
    "**超参数**：\n",
    "- `num_hiddens = 32`：隐状态维度\n",
    "- `num_layers = 2`：层数\n",
    "- `num_heads = 4`：注意力头数\n",
    "- `dropout = 0.1`：Dropout率\n",
    "- `batch_size = 64`：批量大小\n",
    "- `num_steps = 10`：序列长度\n",
    "\n",
    "**训练设置**：\n",
    "- 学习率：`0.005`\n",
    "- 训练轮数：`200`\n",
    "- 设备：GPU（如果可用）\n",
    "\n",
    "**训练说明**：\n",
    "- 依照Transformer架构来实例化编码器-解码器模型\n",
    "- 与序列到序列训练类似，在\"英语-法语\"机器翻译数据集上训练Transformer模型\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 数据准备\n",
    "\n",
    "**数据集**：\n",
    "- \"英语-法语\"机器翻译数据集\n",
    "- 使用`d2l.load_data_nmt`加载\n",
    "\n",
    "**数据预处理**：\n",
    "- 构建源语言和目标语言的词汇表\n",
    "- 批量加载和预处理\n",
    "\n",
    "**任务**：\n",
    "- 序列到序列学习\n",
    "- 输入：英语句子\n",
    "- 输出：法语句子\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 模型实例化\n",
    "\n",
    "**编码器**：\n",
    "- 输入：源语言词汇表大小\n",
    "- 输出：编码后的表示\n",
    "\n",
    "**解码器**：\n",
    "- 输入：目标语言词汇表大小\n",
    "- 输出：目标语言的词元预测\n",
    "\n",
    "**组合**：\n",
    "- 使用`EncoderDecoder`类组合编码器和解码器\n",
    "- 形成完整的序列到序列模型\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 训练过程\n",
    "\n",
    "**训练函数**：\n",
    "- 使用`d2l.train_seq2seq`进行训练\n",
    "- 自动处理训练循环、损失计算、优化等\n",
    "\n",
    "**监控指标**：\n",
    "- 损失值（loss）\n",
    "- 训练速度（tokens/sec）\n",
    "\n",
    "**观察**：\n",
    "- 训练速度：约5200 tokens/sec（GPU）\n",
    "- 损失值：约0.030（训练200轮后）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 翻译测试\n",
    "\n",
    "**测试目的**：\n",
    "- 验证训练好的模型\n",
    "- 计算BLEU分数评估翻译质量\n",
    "\n",
    "**测试句子**：\n",
    "- \"go .\" → \"va !\"\n",
    "- \"i lost .\" → \"j'ai perdu .\"\n",
    "- \"he's calm .\" → \"il est calme .\"\n",
    "- \"i'm home .\" → \"je suis chez moi .\"\n",
    "\n",
    "**BLEU分数**：\n",
    "- 范围：0到1\n",
    "- 1.000：完美翻译\n",
    "- 观察：所有测试句子都达到1.000，说明模型训练良好\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 67,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 翻译测试代码\n",
    "\n",
    "**测试目的**：\n",
    "- 训练结束后，使用Transformer模型将一些英语句子翻译成法语\n",
    "- 计算它们的BLEU分数以评估翻译质量\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-08-18T07:22:31.189851Z"
    },
    "origin_pos": 68,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go . => va !,  bleu 1.000\n",
      "i lost . => j'ai perdu .,  bleu 1.000\n",
      "he's calm . => il est calme .,  bleu 1.000\n",
      "i'm home . => je suis chez moi .,  bleu 1.000\n"
     ]
    }
   ],
   "source": [
    "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
    "for eng, fra in zip(engs, fras):\n",
    "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(\n",
    "        net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
    "    print(f'{eng} => {translation}, ',\n",
    "          f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 注意力权重可视化\n",
    "\n",
    "### 编码器自注意力权重\n",
    "\n",
    "**权重形状**：\n",
    "- `(num_layers, num_heads, num_queries, num_keys)`\n",
    "- 例如：`(2, 4, num_steps, num_steps)`\n",
    "\n",
    "**含义**：\n",
    "- 第1维：编码器层数\n",
    "- 第2维：注意力头数\n",
    "- 第3维：查询位置\n",
    "- 第4维：键位置\n",
    "\n",
    "**特点**：\n",
    "- 查询和键都来自**相同的输入序列**\n",
    "- 通过`valid_lens`避免与填充词元计算注意力\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 编码器自注意力可视化\n",
    "\n",
    "**可视化内容**：\n",
    "- 两层编码器的4个注意力头\n",
    "- 每个头显示查询-键的注意力权重\n",
    "\n",
    "**观察**：\n",
    "- 不同头关注不同的位置\n",
    "- 有些头关注相邻位置（局部模式）\n",
    "- 有些头关注远距离位置（全局模式）\n",
    "- 这体现了多头注意力的优势\n",
    "\n",
    "**意义**：\n",
    "- 理解模型如何关注输入序列\n",
    "- 验证注意力机制是否正常工作\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:22:31.194154Z",
     "iopub.status.busy": "2023-08-18T07:22:31.193869Z",
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     "shell.execute_reply": "2023-08-18T07:22:31.199543Z"
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    "origin_pos": 71,
    "slideshow": {
     "slide_type": "slide"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 10, 10])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc_attention_weights = torch.cat(net.encoder.attention_weights, 0).reshape((num_layers, num_heads,\n",
    "    -1, num_steps))\n",
    "enc_attention_weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:22:31.204505Z",
     "iopub.status.busy": "2023-08-18T07:22:31.203733Z",
     "iopub.status.idle": "2023-08-18T07:22:31.970964Z",
     "shell.execute_reply": "2023-08-18T07:22:31.969901Z"
    },
    "origin_pos": 74,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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    "d2l.show_heatmaps(\n",
    "    enc_attention_weights.cpu(), xlabel='Key positions',\n",
    "    ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],\n",
    "    figsize=(7, 3.5))"
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    "### 解码器注意力权重处理\n",
    "\n",
    "**两种注意力权重**：\n",
    "1. **解码器自注意力权重**：解码器内部的位置关系\n",
    "2. **编码器-解码器注意力权重**：解码器关注编码器的哪些部分\n",
    "\n",
    "**处理步骤**：\n",
    "1. 提取权重\n",
    "2. 用零填充被掩蔽的位置\n",
    "3. 重塑为可视化格式\n",
    "\n",
    "**形状**：\n",
    "- 解码器自注意力：`(num_layers, num_heads, num_queries, num_keys)`\n",
    "- 编码器-解码器注意力：相同形状\n",
    "\n",
    "**注意**：\n",
    "- 查询以BOS（序列开始词元）打头\n",
    "- 然后与后续输出的词元共同组成序列\n"
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     "data": {
      "text/plain": [
       "(torch.Size([2, 4, 6, 10]), torch.Size([2, 4, 6, 10]))"
      ]
     },
     "execution_count": 18,
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    "dec_attention_weights_2d = [head[0].tolist()\n",
    "                            for step in dec_attention_weight_seq\n",
    "                            for attn in step for blk in attn for head in blk]\n",
    "dec_attention_weights_filled = torch.tensor(\n",
    "    pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)\n",
    "dec_attention_weights = dec_attention_weights_filled.reshape((-1, 2, num_layers, num_heads, num_steps))\n",
    "dec_self_attention_weights, dec_inter_attention_weights = \\\n",
    "    dec_attention_weights.permute(1, 2, 3, 0, 4)\n",
    "dec_self_attention_weights.shape, dec_inter_attention_weights.shape"
   ]
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  {
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   "source": [
    "### 解码器自注意力可视化\n",
    "\n",
    "**关键特点**：\n",
    "- **自回归属性**：查询不会对当前位置之后的键值对计算注意力\n",
    "- 这是**掩蔽机制**的体现\n",
    "\n",
    "**可视化内容**：\n",
    "- 两层解码器的4个注意力头\n",
    "- 显示每个查询位置关注哪些已生成的词元\n",
    "\n",
    "**观察**：\n",
    "- 注意力权重呈**下三角**模式\n",
    "- 当前位置只能看到之前的位置\n",
    "- 这保证了自回归生成的正确性\n"
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    "slideshow": {
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    "# Plusonetoincludethebeginning-of-sequencetoken\n",
    "d2l.show_heatmaps(\n",
    "    dec_self_attention_weights[:, :, :, :len(translation.split()) + 1],\n",
    "    xlabel='Key positions', ylabel='Query positions',\n",
    "    titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
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    "### 编码器-解码器注意力可视化\n",
    "\n",
    "**关键特点**：\n",
    "- 查询：解码器的输出位置\n",
    "- 键和值：编码器的输入位置\n",
    "- 通过`enc_valid_lens`避免与填充位置计算注意力\n",
    "\n",
    "**可视化内容**：\n",
    "- 显示解码器每个位置关注编码器的哪些位置\n",
    "- 类似Bahdanau注意力的对齐关系\n",
    "\n",
    "**观察**：\n",
    "- 可以看到翻译时的对齐模式\n",
    "- 例如：生成\"va\"时关注\"go\"\n",
    "- 这验证了注意力机制的有效性\n"
   ]
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   "cell_type": "code",
   "execution_count": 20,
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     "iopub.status.idle": "2023-08-18T07:22:33.326167Z",
     "shell.execute_reply": "2023-08-18T07:22:33.325372Z"
    },
    "origin_pos": 83,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tab": [
     "pytorch"
    ]
   },
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    "d2l.show_heatmaps(\n",
    "    dec_inter_attention_weights, xlabel='Key positions',\n",
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    "## 总结\n",
    "\n",
    "### Transformer的应用\n",
    "\n",
    "**原始应用**：\n",
    "- 序列到序列学习（机器翻译）\n",
    "\n",
    "**现代应用**：\n",
    "- **编码器单独使用**：\n",
    "  - BERT：双向编码器表示\n",
    "  - 文本分类、问答等任务\n",
    "\n",
    "- **解码器单独使用**：\n",
    "  - GPT：生成式预训练\n",
    "  - 文本生成、对话系统\n",
    "\n",
    "- **完整架构**：\n",
    "  - 机器翻译\n",
    "  - 文本摘要\n",
    "  - 对话生成\n"
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    "### Transformer的优势\n",
    "\n",
    "**优势**：\n",
    "- ✅ 完全并行计算\n",
    "- ✅ 长距离依赖建模能力强\n",
    "- ✅ 训练效率高（相比RNN）\n",
    "- ✅ 可解释性强（注意力权重可视化）\n",
    "- ✅ 通用性强（适用于多种任务）\n",
    "\n",
    "**局限**：\n",
    "- ❌ 计算复杂度$O(n^2)$，对长序列慢\n",
    "- ❌ 内存消耗大\n",
    "- ❌ 需要大量数据训练\n",
    "\n",
    "**适用场景**：\n",
    "- 中等长度序列（< 512 tokens）\n",
    "- 需要捕获长距离依赖的任务\n",
    "- 有足够计算资源的情况\n"
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